• ALGAE
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• v.37 no.1
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• pp.63-74
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• 2022

Perennial stipitate kelps are globally distributed and individual species can inhabit broad latitudinal ranges, expressing notably longevous persistence. Despite the foundational role kelps provide to their communities, little is known about the variability in persistence of the stipitate kelps at local spatial scales. We studied the population persistence of Eisenia arborea, a heat- and wave force-tolerant perennial stipitate kelp with a distributional range extending from British Columbia to south of the range limit of all other northeast Pacific kelps, in Baja California Sur, Mexico. Persistence characteristics for E. arborea among sites were compared and used to test the hypothesis that stand persistence varied at local spatial scales around Isla Natividad, a Pacific island off the Baja California peninsula with documented spatiotemporal environmental heterogeneity. Collected individuals around the island were "aged" using the previously validated age estimation technique of counting annual cortical dark rings. After detecting no significant differences among sites in the covariation between estimated ages for collected individuals and stipe length, we utilized in-situ population-level stipe length measurements to more rapidly predict age structures within six stands around the island. Predicted age structures, and associated stand densities, revealed persistence characteristics and density varied at local scales and a strong positive relationship existed between stand density and stand mean and maximum ages. We speculate that stands responded differently to deterministic influences (e.g., the 2014-2016 marine heatwave and / or competition with Macrocystis) resulting in heterogenous local persistence of this foundation species.

• Journal of Korean Institute of Industrial Engineers
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• v.42 no.2
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• pp.96-111
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• 2016

Effort to reduce energy consumptions or CO2 emissions is global trend. To follow this trend, spatial studies related to characteristics affecting energy consumptions or CO2 emissions have been conducted, but only with the focus on spatial dependence, not on spatial heterogeneity. The aim of this study is to investigate spatial heterogeneity patterns of CO2 emission based on socio-economic factors, land-use characteristics and traffic infrastructure of Seoul city. Geographically Weighted Regression (GWR) analysis was performed with 423 administrative district data in Seoul. The results suggest that population and employment densities, road density and railway length in most districts are found to have positive impact on the CO2 emissions. Residential and green area densities also have the highest positive impact on CO2 emissions in most districts of Gangnam-gu. The resulting model can be used to identify the spatial patterns of CO2 emissions at district level in Seoul. Eventually it can contribute to local energy policy and planning of metropolitan area.

• Journal of Korean Society for Geospatial Information Science
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• v.24 no.4
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• pp.75-81
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• 2016

This study focused on exploring real transaction price of apartment and spatial and temporal heterogeneity of the variables that influence real transaction price of apartment from the spatial and temporal perspective. As independent variables that are considered to influence real transaction price of apartment, transport, local characteristics, educational conditions, population, and economic characteristics were taken into account. Accordingly, the influence of independent variables and spatial distribution pattern were analyzed from the global and local aspects. The spatial and temporal changing patterns of real transaction price of apartment which is a dependent variable were analyzed. First, to establish an analysis model, OLS analysis and GWR analysis were conducted, and thereby more efficient and proper model was selected. Secondly, to find spatial and temporal heterogeneity of independent variables with the use of the selected GWR model, Local $R^2$ was used for local analysis. Thirdly, to look into spatial distribution of independent variables, kriging analysis was carried out. Therefore, based on the results, it is considered that it is possible to carry out more microscopic housing submarket analysis and lay the foundation for establishing a policy on real property.

• Journal of the Korean association of regional geographers
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• v.16 no.5
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• pp.610-622
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• 2010

The proportion of overweight and obese individuals in the United States has been continuously increasing up to recently. Many studies related to obesity have concentrated on jurisdictional levels of aggregation, making it very difficult to dearly illustrate at risk regions. In other words, little research has been conducted in relation to spatial patterns considering spatial dependency and heterogeneity by spatial autocorrelation models over space. In response, this research analyzes spatial patterns between overweight/obesity and risk factors, such as high blood pressure and diabetes, over space. Specifically, the Moran''s I and Geary''s C will be conducted for global and local measures. What is more, the Ordinary Least Square (OLS) linear regression and Geographically Weighted Regression methods will be applied to identify spatial dependency and spatial heterogeneity. Data provided by the Behavioral Risk Factor Surveillance System (BRFSS) have Body-Mass Index (BMI) rates, containing 4 rates of under, healthy, overweight, and obesity. In addition, high blood pressure and diabetes rates in the United States will be used as independent variables. Lastly, we are confident that this research will be beneficial for a decision maker to make a prevention plan for obesity.

• Journal of Microbiology and Biotechnology
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• v.32 no.10
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• pp.1275-1283
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• 2022

Understanding soil microbial community structure in the Arctic is essential for predicting the impact of climate change on interactions between organisms living in polar environments. The hypothesis of the present study was that soil microbial communities and soil chemical characteristics would vary depending on their associated plant species and local environments in Arctic mature soils. We analyzed soil bacterial communities and soil chemical characteristics from soil without vegetation (bare soil) and rhizosphere soil of three Arctic plants (Cassiope tetragona [L.] D. Don, Dryas octopetala L. and Silene acaulis [L.] Jacq.) in different local environments (coal-mined site and seashore-adjacent site). We did not observe any clear differences in microbial community structure in samples belonging to different plant rhizospheres; however, samples from different environmental sites had distinct microbial community structure. The samples from coal-mined site had a relatively higher abundance of Bacteroidetes and Firmicutes. On the other hand, Acidobacteria was more prevalent in seashore-adjacent samples. The relative abundance of Proteobacteria and Acidobacteria decreased toward higher soil pH, whereas that of Bacteroidetes and Firmicutes was positively correlated with soil pH. Our results suggest that soil bacterial community dissimilarity can be driven by spatial heterogeneity in deglaciated mature soil. Furthermore, these results indicate that soil microbial composition and relative abundance are more affected by soil pH, an abiotic factor, than plant species, a biotic factor.

• Asian Journal of Atmospheric Environment
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• v.8 no.2
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• pp.69-80
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• 2014

It is often assumed that atmospheric observations at remote sites represent long-range transport of airborne material, and local influences are overlooked. We evaluated the impact of local input on the rainwater composition at Gosan Station, a strategic site for monitoring the continental outflow from Asia. We analyzed a 14-year record of rainwater chemical composition archived by the Korea Meteorological Administration and detected local terrestrial contribution for nitrate, sulfate and ammonium. We also measured the chemical composition of rainwater sampled simultaneously at multiple locations within the premises of the Gosan Station, from which local influence with meter-scale spatial heterogeneity could be discerned. We estimate that the local input accounted for at least ~10% of the wet deposition of nitrogen and ~12% of the wet deposition of sulfur during the 14 years. This highlights the significance of the local influence, which should be carefully assessed when interpreting atmospheric observations at this site.

• Journal of the Korean Association of Geographic Information Studies
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• v.22 no.1
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• pp.1-18
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• 2019

This research measured and visualized the spatial dependency and the spatial heterogeneity of the small business in Cheonan-si, Asan-si with $100m{\times}100m$ grids based on global and local spatial autocorrelation. First, we confirmed positive spatial autocorrelation of small business in the research area using Moran's I Index, which is ESDA(Exploratory Spatial Data Analysis). And then, through Getis-Ord $GI{\ast}$, one kind of LISA(Local Indicators of Spatial Association), local patterns of spatial autocorrelation were visualized. These verified that Spatial Regression Model is valid for the location factor analysis on small business commercial buildings. Next, GWR(Geographically Weighted Regression) was used to analyze the spatial relations between the distribution of small business, hourly mobile traffic-based floating population, land use attributes index, residence, commercial building, road networks, and the node of traffic networks. Final six variables were applied and the accessibility to bus stops, afternoon time floating population, and evening time floating population were excluded due to multicollinearity. By this, we demonstrated that GWR is statistically improved compared to OLS. We visualized the spatial influence of the individual variables using the regression coefficients and local coefficients of determinant of the six variables. This research applied the measured population information in a practical way. Reflecting the dynamic information of the urban people using the commercial area. It is different from other studies that performed commercial analysis. Finally, this research has a differentiated advantage over the existing commercial area analysis in that it employed hourly changing commercial service population data and it applied spatial statistical models to micro spatial units. This research proposed new framework for the commercial analysis area analysis.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.12 no.5
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• pp.28-41
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• 2009

Because fire has significant impacts on fauna and flora in forest ecosystems, as well as socioeconomic influences to local community, it has been an important field of study for decades. One of the most common ways to reduce fire risk is to enhance fire-resilience of forest through fuel treatments including thinning and prescribed burning. Since fuel treatment can't be practiced over all forested areas, appropriate and effective strategies are needed. The present study aims to look at the relationship between spatial characteristics of forest structure measured with landscape pattern metrics and burn severity to provide guidelines for effective fuel treatments. Samchuck fire was selected for the study, and 232 grids covering the study areas were generated, and the grid size was 1km. The burn severity is measured with dNBR derived from satellite imagery, and spatial characteristics of forest structure were measured using FRAGSTATS for both landscape and class levels for each 1km grid. The results of this study strongly indicated that heterogeneity in composition and configuration of forests may significantly reduce burn severity. By enhancing heterogeneity of forests, fuel treatments for fire-resilience forest could be more effective.

• Journal of the Korean Geographical Society
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• v.39 no.2
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• pp.212-228
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• 2004

This paper is concerned with an important aspect of regional income convergence, ${\beta}$-convergence, which refers to the negative relationship between initial income levels and income growth rates of regions over a period of time. The common research framework on ${\beta}$-convergence which is based on OLS regression models has two drawbacks. First, it ignores spatially autocorrelated residuals. Second, it does not provide any way of exploring spatial heterogeneity across regions in terms of ${\beta}$-convergence. Given that empirical studies on ${\beta}$-convergence need to be edified by spatial data analysis, this paper aims to: (1) provide a critical review of empirical studies on ${\beta}$-convergence from a spatial perspective; (2) investigate spatio-temporal income dynamics across the U.S. labor market areas for the last 30 years (1969-1999) by fitting spatial regression models and applying bivariate ESDA techniques. The major findings are as follows. First, the hypothesis of ${\beta}$-convergence was only partially evidenced, and the trend substantively varied across sub-periods. Second, a SAR model indicated that ${\beta}$-coefficient for the entire period was not significant at the 99％ confidence level, which may lead to a conclusion that there is no statistical evidence of regional income convergence in the US over the last three decades. Third, the results from bivariate ESDA techniques and a GWR model report that there was a substantive level of spatial heterogeneity in the catch-up process, and suggested possible spatial regimes. It was also observed that the sub-periods showed a substantial level of spatio-temporal heterogeneity in ${\beta}$-convergence: the catch-up scenario in a spatial sense was least pronounced during the 1980s.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.125-125
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• 2020

European Space Agency's Sentinel-1 has an improved spatial and temporal resolution, as compared to previous satellite data such as Envisat Advanced SAR (ASAR) or Advanced Scatterometer (ASCAT). Thus, the assumption used for low-resolution retrieval algorithms used by ENVISAT ASAR or ASCAT is not applicable to Sentinel-1, because a higher degree of land surface heterogeneity should be considered for retrieval. The assumption of homogeneity over land surface is not valid any more. In this study, considering that soil roughness is one of the key parameters sensitive to soil moisture retrievals, various approaches are discussed. First, soil roughness is spatially inverted from Sentinel-1 backscattering over Yanco sites in Australia. Based upon this, Artificial Neural Networks data (feedforward multiplayer perception, MLP, Levenberg-Marquadt algorithm) are compared with Fractal approach (brownian fractal, Hurst exponent of 0.5). When using ANNs, training data are achieved from theoretical forward scattering models, Integral Equation Model (IEM). and Sentinel-1 measurements. The network is trained by 20 neurons and one hidden layer, and one input layer. On the other hand, fractal surface roughness is generated by fitting 1D power spectrum model with roughness spectra. Fractal roughness profile is produced by a stochastic process describing probability between two points, and Hurst exponent, as well as rms heights (a standard deviation of surface height). Main interest of this study is to estimate a spatial variability of roughness without the need of local measurements. This non-local approach is significant, because we operationally have to be independent from local stations, due to its few spatial coverage at the global level. More fundamentally, SAR roughness is much different from local measurements, Remote sensing data are influenced by incidence angle, large scale topography, or a mixing regime of sensors, although probe deployed in the field indicate point data. Finally, demerit and merit of these approaches will be discussed.